A load scanning apparatus for taking physical measurements from a load. The load scanning apparatus has a scanning robot including a plurality of sensors arranged in an array spanning substantially across at least one load dimension in a first direction. The array of sensors moves together in a second direction, in a scanning plane. The plurality of sensors are configured to take images of the load from the scanning plane, and are configured to capture distance information about the distance of said load from the scanning plane.

A system for identifying accurate assembly of a component to a workpiece is disclosed. The system includes a light source for projecting light indicia onto the component assembled to the workpiece. A controller includes an artificial intelligence (AI) element defining a machine learning model that establishes a convoluted neural network trained by stored images of light indicia projected onto the component assembled to the workpiece. An imager includes an image sensor system for imaging the workpiece and signaling a current image of the workpiece to the controller. The machine learning model directs inspection of the workpiece to the light indicia imaged by said imager. The AI element determines disposition of the component disposed upon the workpiece through the neural network identifying distortions of the light indicia in the current image.

A method includes: receiving a recycled wood workpiece populated with a set of metal fasteners; accessing an internal imaging scan; detecting the set of metal fasteners embedded in the recycled wood workpiece based on internal features detected in the internal imaging scan; for each metal fastener in the set of metal fasteners, extracting an initial position and an initial orientation of the metal fastener from the internal imaging scan; generating a virtual model of the recycled wood workpiece based on the internal imaging scan; accessing an image captured by an optical sensor; detecting a first metal fastener in the recycled wood workpiece; deriving a first position and a first orientation of the first metal fastener; and, in response to identifying the first metal fastener analogous to an initial metal fastener in the virtual model, isolating the first metal fastener in the virtual model and generating a fastener removal schedule.

An image log of a subsurface region may be divided into multiple image log segments. The multiple image log segments may be processed through a computer vision neural network to identify both (1) the types of subsurface features within the subsurface region, and (2) the locations of the subsurface features within the subsurface region.

The present disclosure describes methods and systems for virtually calibrating geometric sensors with overlapping fields of view. In some embodiments, a geometric sensor may be virtually calibrated by applying a correction value to profile data obtained by the geometric sensor to generate adjusted profile data. The correction factor may be determined based at least in part on X-Y offsets and/or rotational offsets of prior profile data obtained by the geometric sensor relative to corresponding profile data obtained by a reference geometric sensor, and may be recalculated or updated as new sets of profile data are obtained. The adjusted profile data may be used in place of the original profile data in various data processing operations to functionally offset a positional error of the geometric sensor.

Provided herein are systems, media, and methods for roll-to-roll material (e.g. fabric) defect detection and/or quality control based on data received from an optical detection.

A method includes: receiving a recycled wood workpiece populated with a set of metal fasteners; accessing an internal imaging scan; detecting the set of metal fasteners embedded in the recycled wood workpiece based on internal features detected in the internal imaging scan; for each metal fastener in the set of metal fasteners, extracting an initial position and an initial orientation of the metal fastener from the internal imaging scan; generating a virtual model of the recycled wood workpiece based on the internal imaging scan; accessing an image captured by an optical sensor; detecting a first metal fastener in the recycled wood workpiece; deriving a first position and a first orientation of the first metal fastener; and, in response to identifying the first metal fastener analogous to an initial metal fastener in the virtual model, isolating the first metal fastener in the virtual model and generating a fastener removal schedule.

A method includes: receiving a recycled wood workpiece populated with a set of metal fasteners; accessing an internal imaging scan; detecting the set of metal fasteners embedded in the recycled wood workpiece based on internal features detected in the internal imaging scan; for each metal fastener in the set of metal fasteners, extracting an initial position and an initial orientation of the metal fastener from the internal imaging scan; generating a virtual model of the recycled wood workpiece based on the internal imaging scan; accessing an image captured by an optical sensor; detecting a first metal fastener in the recycled wood workpiece; deriving a first position and a first orientation of the first metal fastener; and, in response to identifying the first metal fastener analogous to an initial metal fastener in the virtual model, isolating the first metal fastener in the virtual model and generating a fastener removal schedule.

A method includes: receiving a recycled wood workpiece populated with a set of metal fasteners; accessing an internal imaging scan; detecting the set of metal fasteners embedded in the recycled wood workpiece based on internal features detected in the internal imaging scan; for each metal fastener in the set of metal fasteners, extracting an initial position and an initial orientation of the metal fastener from the internal imaging scan; generating a virtual model of the recycled wood workpiece based on the internal imaging scan; accessing an image captured by an optical sensor; detecting a first metal fastener in the recycled wood workpiece; deriving a first position and a first orientation of the first metal fastener; and, in response to identifying the first metal fastener analogous to an initial metal fastener in the virtual model, isolating the first metal fastener in the virtual model and generating a fastener removal schedule.

Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based moisture level detection models, are used to accurately identify moisture content and the specific locations of the moisture on an entire surface of a veneer sheet or other wood product and provide moisture level prediction data for the veneer sheet or other wood product. Based on the moisture level prediction data for a given wood product, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.